Predictor-based random search could also be used as a replacement for the NSGA-II evolutionary search algorithm [1]} in DONNA. However, NSGA-II is known to be more sample efficient than random search in a multi-objective setting [2]}. This is also illustrated in Figure REF , where NSGA-II finds networks with a higher predicted accuracy than random search, given the 190 (400M), 77 (500M) and 19 (600M) samples for every target. In this NSGA-II, a total of 2500 samples was generated and measured during the search, covering the full search-space ranging from 150-800M operations.
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